Previous work has demonstrated the utility of exploring the reverse causal interpretation of large scale data sets using HYPs as opposed to reasoning downstream from the data [ 12- 14].
The value-relevance of this study comes from the participants' values – i.e. their normative reasoning – not from the data analysis.
From the discussion about scientific reasoning above, Data, Patterns from the data, and Theories are selected as major components of scientific reasoning (Anderson 2006; Koslowski 1996; Kuhn and Franklin 2007; Zimmerman 2007).
The application components use (a subset of) the core and domain-specific ontologies to perform sophisticated reasoning on the data they receive from the SCB.
The Web Ontology Language allows to classify the concepts retrieved from gbXML and, due to the formal definition of the language, also reasoning on the data becomes possible.
Our deductive reasoning converges with the data from biological experiments describing BA44 as part of the human homologue of monkey area F5 [9], [9], a main component of a system coined as the mirror neuron system (MNS) [9], [10].
The starting point of model building may be a hypothesis that has been derived via induction, i.e. the reasoning from data to ideas (e.g. psychiatric and neurological nosology, therapeutic criteria, identified endophenotypes; (see [ 17, 167]), or abduction or deduction, i.e. the reasoning from ideas to data (e.g. observed behavioral abnormalities; induced or naturally occurring mutations [ 168]).
Inductive methods involve a process of reasoning from the particular to the general, or from real-world data to general relationships.
The observation from the data is that the two formats can potentially evoke more 'expert' methods of diagnostic reasoning processes such as scheme utilization or pattern recognition.
They are all about knowing a fact from a factoid, reasoning from data to underlying patterns and practical implications, all while feeding careful observation through the strainer of valid logic.
